Read in SF trees data

sf_trees <- read.csv(here("data", "sf_trees","sf_trees.csv"))

Basic wrangling reminders

Refresh some skills for data wrangling and summary statistics using functions in the dplyr package.

Find the top 5 highest observations of trees by legal_status, do some wrangling, make a graph

# counts of trees by legal status

top_5_status <- sf_trees %>%
  count(legal_status) %>% 
  drop_na(legal_status) %>%
  rename(tree_count = n) %>% 
  relocate(tree_count) %>% 
  slice_max(tree_count, n =5)

Make a graph of the top 5 observations by legal status

ggplot(data = top_5_status, aes(x= fct_reorder(legal_status, tree_count), y = tree_count)) + geom_col()+
  labs(x = "Legal Status", y = "Tree Count") +
  coord_flip()+
  theme_classic()

A few more data wrangling refresher examples

Only want to keep oberservations for Blackwood Acacia Trees

blackwood_acacia <- sf_trees %>% 
  filter(str_detect(species, "Blackwood Acacia")) %>% 
  select(legal_status, date, latitude, longitude)

# preview of a map, R does not know these are longitude and latitude, just reads them as numbers
ggplot(data = blackwood_acacia, aes(x= longitude, y = latitude)) + geom_point()
## Warning: Removed 27 rows containing missing values (geom_point).

todyr::separate and unite() functions

useful for combining or separating columns

sf_trees_sep <- sf_trees %>% 
  separate(species, into = c("spp_scientific", "spp_common"), sep = "::")
sf_trees_unite <- sf_trees %>% 
  unite("id_status", tree_id:legal_status, sep = "_cool!_")

Make actual maps of blackwood acacia trees in SF

st_as_sf() to convert latitude and longitude to spatial coordinates

#recognizing data as spatial
blackwood_acacia_sp <- blackwood_acacia %>% 
  drop_na(longitude, latitude) %>% 
  st_as_sf(coords = c("longitude", "latitude"))

# does not have a set coordinate system, have to set it

st_crs(blackwood_acacia_sp) = 4326

ggplot(data = blackwood_acacia_sp) +
  geom_sf(color = "darkgreen")

Read in SF roads shapefile:

sf_map <- read_sf(here("data", "sf_map", "tl_2017_06075_roads.shp"))

st_transform(sf_map, 4326) #need to use st_transform, because the map already had a coordinate system, so we had to change it
## Simple feature collection with 4087 features and 4 fields
## geometry type:  LINESTRING
## dimension:      XY
## bbox:           xmin: -122.5136 ymin: 37.70813 xmax: -122.3496 ymax: 37.83213
## geographic CRS: WGS 84
## # A tibble: 4,087 x 5
##    LINEARID   FULLNAME     RTTYP MTFCC                                  geometry
##  * <chr>      <chr>        <chr> <chr>                          <LINESTRING [°]>
##  1 110498938… Hwy 101 S O… M     S1400 (-122.4041 37.74842, -122.404 37.7483, -…
##  2 110498937… Hwy 101 N o… M     S1400 (-122.4744 37.80691, -122.4746 37.80684,…
##  3 110366022… Ludlow Aly … M     S1780 (-122.4596 37.73853, -122.4596 37.73845,…
##  4 110608181… Mission Bay… M     S1400 (-122.3946 37.77082, -122.3929 37.77092,…
##  5 110366689… 25th Ave N   M     S1400 (-122.4858 37.78953, -122.4855 37.78935,…
##  6 110368970… Willard N    M     S1400 (-122.457 37.77817, -122.457 37.77812, -…
##  7 110368970… 25th Ave N   M     S1400 (-122.4858 37.78953, -122.4858 37.78952,…
##  8 110498933… Avenue N     M     S1400 (-122.3643 37.81947, -122.3638 37.82064,…
##  9 110368970… 25th Ave N   M     S1400  (-122.4854 37.78983, -122.4858 37.78953)
## 10 110367749… Mission Bay… M     S1400 (-122.3865 37.77086, -122.3878 37.77076,…
## # … with 4,077 more rows
ggplot(data = sf_map)+
  geom_sf()

Combine blackwood acacia tree observerations and road map

ggplot()+
  geom_sf(data = sf_map, size = 0.1, color = "darkgray") +
  geom_sf(data = blackwood_acacia_sp, color = "red", size=0.5)+
  theme_void()

Now to create an interactive map

tmap_mode("view")
## tmap mode set to interactive viewing
tm_shape(blackwood_acacia_sp) + 
  tm_dots()